sklearndf.classification.wrapper.LinearDiscriminantAnalysisWrapperDF

class sklearndf.classification.wrapper.LinearDiscriminantAnalysisWrapperDF(*args, **kwargs)

DF wrapper for sklearn.discriminant_analysis.LinearDiscriminantAnalysis.

Bases:

ClassifierWrapperDF [LinearDiscriminantAnalysis], NComponentsDimensionalityReductionWrapperDF [LinearDiscriminantAnalysis]

Metaclasses:

EstimatorWrapperDFMeta, ABCMeta

Parameters:

• args (Any) – transformer parameters

• kwargs (Any) – transformer keyword parameters

Method summary

clone	Make an unfitted clone of this estimator.
decision_function	Compute the decision function for the given inputs.
fit	Fit this estimator using the given inputs.
fit_transform	Fit this transformer using the given inputs, then transform the inputs.
from_fitted	Make a new wrapped DF estimator, delegating to a given native estimator that has already been fitted.
get_metadata_routing	See sklearn.utils.get_metadata_routing()
get_params	Get the parameters for this estimator.
inverse_transform	Inverse-transform the given inputs.
predict	Predict outputs for the given inputs.
predict_log_proba	Predict class log-probabilities for the given inputs.
predict_proba	Predict class probabilities for the given inputs.
score	Score this learner using the given inputs and outputs.
set_output	See sklearn.utils.set_output()
set_params	Set the parameters of this estimator.
set_score_request	See sklearn.utils.RequestMethod.__get__.<locals>.func()
to_expression	Render this object as an expression.
transform	Transform the given inputs.

Attribute summary

COL_FEATURE	Name assigned to an Index or a Series with the names of the features used to fit a EstimatorDF.
COL_FEATURE_ORIGINAL	Name assigned to a Series with the original feature names before transformation.
COL_PREDICTION	Name of Series objects containing the predictions of single-output learners.
classes_	Get the classes predicted by this classifier as a numpy array of class labels for single-output problems, or a list of such arrays for multi-output problems
feature_names_in_	The pandas column index with the names of the features used to fit this estimator.
feature_names_original_	A pandas series, mapping the output features resulting from the transformation to the original input features.
feature_names_out_	A pandas column index with the names of the features produced by this transformer
is_fitted	True if this object is fitted, False otherwise.
n_features_in_	The number of features used to fit this estimator.
n_outputs_	The number of outputs used to fit this estimator.
native_estimator	The native estimator that this wrapper delegates to.
output_names_	The name(s) of the output(s) this supervised learner was fitted to.

Definitions

clone()

Make an unfitted clone of this estimator.

Return type:

LinearDiscriminantAnalysisWrapperDF

Returns:

the unfitted clone

decision_function(X, **predict_params)

Compute the decision function for the given inputs.

The inputs must have the same features as the inputs used to fit this learner. The features can be provided in any order since they are identified by their column names.

Parameters:

• X (Union[Series, DataFrame]) – input data frame with observations as rows and features as columns

• predict_params (Any) – optional keyword parameters as required by specific learner implementations

Return type:

Union[Series, DataFrame]

Returns:

a data frame with observations as rows and classes as columns, and values as the raw values predicted per observation and class; for multi-output classifiers, a list of one observation/class data frames per output

fit(X, y=None, **fit_params)

Fit this estimator using the given inputs.

Parameters:

• X (Union[DataFrame, Series]) – input data frame with observations as rows and features as columns

• y (Union[Series, DataFrame, None]) – an optional series or data frame with one or more outputs

• fit_params (Any) – additional keyword parameters as required by specific estimator implementations

Return type:

LinearDiscriminantAnalysisWrapperDF

Returns:

self

fit_transform(X, y=None, **fit_params)

Fit this transformer using the given inputs, then transform the inputs.

Parameters:

• X (Union[Series, DataFrame]) – input data frame with observations as rows and features as columns

• y (Optional[Series]) – an optional series or data frame with one or more outputs

• fit_params (Any) – additional keyword parameters as required by specific transformer implementations

Return type:

DataFrame

Returns:

the transformed inputs

classmethod from_fitted(estimator, features_in, n_outputs)

Make a new wrapped DF estimator, delegating to a given native estimator that has already been fitted.

Parameters:

• estimator (LinearDiscriminantAnalysis) – the fitted native estimator to use as the delegate

• features_in (Index) – the column names of X used for fitting the estimator

• n_outputs (int) – the number of outputs in y used for fitting the estimator

Return type:

LinearDiscriminantAnalysisWrapperDF

Returns:

the wrapped data frame estimator

get_metadata_routing()

See sklearn.utils.get_metadata_routing()

get_params(deep=True)

Get the parameters for this estimator.

Parameters:

deep (bool) – if True, return the parameters for this estimator, and for any sub-estimators contained in this estimator

Return type:

Mapping[str, Any]

Returns:

a mapping of parameter names to their values

inverse_transform(X)

Inverse-transform the given inputs.

The inputs must have the same features as the inputs used to fit this transformer. The features can be provided in any order since they are identified by their column names.

Parameters:

X (Union[Series, DataFrame]) – input data frame with observations as rows and features as columns

Return type:

DataFrame

Returns:

the reverse-transformed inputs

predict(X, **predict_params)

Predict outputs for the given inputs.

The inputs must have the same features as the inputs used to fit this learner. The features can be provided in any order since they are identified by their column names.

Parameters:

• X (Union[Series, DataFrame]) – input data frame with observations as rows and features as columns

• predict_params (Any) – optional keyword parameters as required by specific learner implementations

Return type:

Union[Series, DataFrame]

Returns:

predictions per observation as a series, or as a data frame in case of multiple outputs

predict_log_proba(X, **predict_params)

Predict class log-probabilities for the given inputs.

The inputs must have the same features as the inputs used to fit this learner. The features can be provided in any order since they are identified by their column names.

Parameters:

• X (Union[Series, DataFrame]) – input data frame with observations as rows and features as columns

• predict_params (Any) – optional keyword parameters as required by specific learner implementations

Return type:

Union[DataFrame, list[DataFrame]]

Returns:

a data frame with observations as rows and classes as columns, and values as log-probabilities per observation and class; for multi-output classifiers, a list of one observation/class data frames per output

predict_proba(X, **predict_params)

Predict class probabilities for the given inputs.

The inputs must have the same features as the inputs used to fit this learner. The features can be provided in any order since they are identified by their column names.

Parameters:

• X (Union[Series, DataFrame]) – input data frame with observations as rows and features as columns

• predict_params (Any) – optional keyword parameters as required by specific learner implementations

Return type:

Union[DataFrame, list[DataFrame]]

Returns:

a data frame with observations as rows and classes as columns, and values as probabilities per observation and class; for multi-output classifiers, a list of one observation/class data frames per output

score(X, y, sample_weight=None)

Score this learner using the given inputs and outputs.

Parameters:

• X (Union[Series, DataFrame]) – data frame with observations as rows and features as columns

• y (Series) – a series or data frame with the true outputs per observation

• sample_weight (Optional[Series]) – optional series of scalar weights, for calculating the resulting score as the weighted mean of the scores for the individual predictions

Return type:

float

Returns:

the score

set_output(*, transform=None)

See sklearn.utils.set_output()

set_params(**params)

Set the parameters of this estimator.

Valid parameter keys can be obtained by calling get_params().

Parameters:

params (Any) – the estimator parameters to set

Return type:

LinearDiscriminantAnalysisWrapperDF

Returns:

self

set_score_request()

See sklearn.utils.RequestMethod.__get__.<locals>.func()

to_expression()

Render this object as an expression.

Return type:

Expression

Returns:

the expression representing this object

transform(X)

Transform the given inputs.

The inputs must have the same features as the inputs used to fit this transformer. The features can be provided in any order since they are identified by their column names.

Parameters:

X (Union[Series, DataFrame]) – input data frame with observations as rows and features as columns

Return type:

DataFrame

Returns:

the transformed inputs

COL_FEATURE = 'feature'

Name assigned to an Index or a Series with the names of the features used to fit a EstimatorDF.

See feature_names_in_() and feature_names_original_().

COL_FEATURE_ORIGINAL = 'feature_original'

Name assigned to a Series with the original feature names before transformation.

See feature_names_original_().

COL_PREDICTION = 'prediction'

Name of Series objects containing the predictions of single-output learners.

See predict().

property classes_: ndarray[tuple[int, ...], dtype[Any]] | list[ndarray[tuple[int, ...], dtype[Any]]]

Get the classes predicted by this classifier as a numpy array of class labels for single-output problems, or a list of such arrays for multi-output problems

Raises:

AttributeError – this classifier is not fitted

property feature_names_in_: Index

The pandas column index with the names of the features used to fit this estimator.

Raises:

AttributeError – this estimator is not fitted

property feature_names_original_: Series

A pandas series, mapping the output features resulting from the transformation to the original input features.

The index of the resulting series consists of the names of the output features; the corresponding values are the names of the original input features.

Raises:

AttributeError – this transformer is not fitted

property feature_names_out_: Index

A pandas column index with the names of the features produced by this transformer

Raises:

AttributeError – this transformer is not fitted

property is_fitted: bool

True if this object is fitted, False otherwise.

property n_features_in_: int

The number of features used to fit this estimator.

Raises:

AttributeError – this estimator is not fitted

property n_outputs_: int

The number of outputs used to fit this estimator.

Raises:

AttributeError – this estimator is not fitted

property native_estimator: LinearDiscriminantAnalysisWrapperDF

The native estimator that this wrapper delegates to.

property output_names_: list[str]

The name(s) of the output(s) this supervised learner was fitted to.

Raises:

sklearn.exceptions.NotFittedError – this estimator is not fitted